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基于结果的分析性能规范:现状和未来挑战。

Outcome-based analytical performance specifications: current status and future challenges.

机构信息

Department of Chemical Pathology, 441551 New South Wales Health Pathology, Prince of Wales Hospital , Sydney, Australia.

School of Public Health, University of Sydney, Sydney, Australia.

出版信息

Clin Chem Lab Med. 2024 Jun 6;62(8):1474-1482. doi: 10.1515/cclm-2024-0125. Print 2024 Jul 26.

DOI:10.1515/cclm-2024-0125
PMID:38836433
Abstract

Analytical performance specifications (APS) based on outcomes refer to how 'good' the analytical performance of a test needs to be to do more good than harm to the patient. Analytical performance of a measurand affects its clinical performance. Without first setting clinical performance requirements, it is difficult to define how good analytically the test needs to be to meet medical needs. As testing is indirectly linked to health outcomes through clinical decisions on patient management, often simulation-based studies are used to assess the impact of analytical performance on the probability of clinical outcomes which is then translated to Model 1b APS according to the Milan consensus. This paper discusses the related key definitions, concepts and considerations that should assist in finding the most appropriate methods for deriving Model 1b APS. We review the advantages and limitations of published methods and discuss the criteria for transferability of Model 1b APS to different settings. We consider that the definition of the clinically acceptable misclassification rate is central to Model 1b APS. We provide some examples and guidance on a more systematic approach for first defining the clinical performance requirements for tests and we also highlight a few ideas to tackle the future challenges associated with providing outcome-based APS for laboratory testing.

摘要

基于结果的分析性能规格 (APS) 是指测试的分析性能需要达到何种程度才能对患者产生更多的益处而不是危害。测量物的分析性能会影响其临床性能。如果不首先设定临床性能要求,就很难定义测试需要在分析上达到多好的水平才能满足医疗需求。由于测试通过患者管理的临床决策间接与健康结果相关联,因此通常使用基于模拟的研究来评估分析性能对临床结果概率的影响,然后根据米兰共识将其转换为模型 1b APS。本文讨论了相关的关键定义、概念和注意事项,这些都有助于找到最适合的方法来推导模型 1b APS。我们回顾了已发表方法的优缺点,并讨论了将模型 1b APS 转移到不同环境的可转移性标准。我们认为,临床可接受的错误分类率的定义是模型 1b APS 的核心。我们提供了一些示例和更系统的方法来首先定义测试的临床性能要求的指导,并强调了一些应对与实验室检测相关的基于结果的 APS 相关未来挑战的想法。

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Outcome-based analytical performance specifications: current status and future challenges.基于结果的分析性能规范:现状和未来挑战。
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引用本文的文献

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Clin Chem Lab Med. 2025 Jan 28;63(7):1292-1300. doi: 10.1515/cclm-2024-1195. Print 2025 Jun 26.
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A software tool for applying Bayes' theorem in medical diagnostics.一种用于在医学诊断中应用贝叶斯定理的软件工具。
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